This paper introduces DYNUS, an uncertainty-aware trajectory planner designed
for dynamic unknown environments. Operating in such settings presents many
challenges — most notably, because the agent cannot predict the ground-truth
future paths of obstacles, a previously planned trajectory can become unsafe at
any moment, requiring rapid replanning to avoid collisions.
Recently developed planners have used soft-constraint approaches to achieve
the necessary fast computation times; cependant, these methods do not guarantee
collision-free paths even with static obstacles. In contrast, hard-constraint
methods ensure collision-free safety, but typically have longer computation
times.
To address these issues, we propose three key contributions. First, the DYNUS
Global Planner (DGP) and Temporal Safe Corridor Generation operate in
spatio-temporal space and handle both static and dynamic obstacles in the 3D
environment. Second, the Safe Planning Framework leverages a combination of
exploratory, safe, and contingency trajectories to flexibly re-route when
potential future collisions with dynamic obstacles are detected. Finally, the
Fast Hard-Constraint Local Trajectory Formulation uses a variable elimination
approach to reduce the problem size and enable faster computation by
pre-computing dependencies between free and dependent variables while still
ensuring collision-free trajectories.
We evaluated DYNUS in a variety of simulations, including dense forests,
confined office spaces, cave systems, and dynamic environments. Our experiments
show that DYNUS achieves a success rate of 100% and travel times that are
approximately 25.0% faster than state-of-the-art methods. We also evaluated
DYNUS on multiple platforms — a quadrotor, a wheeled robot, and a quadruped —
in both simulation and hardware experiments.

Cet article explore les excursions dans le temps et leurs implications.

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